MR diffusion tensor imaging (DTI) of the brain provides a unique tool for both visualizing directionality, and assessing intactness of white matter fiber tracts in vivo. In white matter tractography, the anisotropic diffusion tensor is used to delineate fiber tract direction, and trace brain connections from voxel to voxel. For quantitative analyses and diagnoses of possible white matter anomalies, parameters derived from the diffusion tensor can be compared between health and disease. Fitting diffusion data from heterogeneous white matter voxels to a single tensor can lead to errors in both the assessment of white matter tract disruption and the computed tract direction. A method is proposed for effectively resolving two tract directions in a subset of voxels that conform to predetermined criteria for containing no more than two tracts. After fitting the single tensor, the underlying constrained two-tensor model has only 4 free parameters. As a result the necessary MR imaging time is drastically reduced, enabling widespread clinical use. In preliminary simulations, as well as in tests on acquired human DTI data, the model appears robust and useful. The principal aim of the research described in this proposal is to rigorously test the performance of the model on simulated data and on data acquired from an imaging phantom containing fiber bundles crossing each other at known angles. The simulated and in-vitro data will also be used in order to optimize the fitting method itself, and to evaluate the sensitivity of the method to noise, and to details of the tract configuration such as angle separation and relative size. The criteria defining for which voxels this model is suitable will be investigated. A variety of diseases, in which white matter injury is known or hypothesized, have been investigated with DTI (multiple sclerosis, schizophrenia, amyotrophic lateral sclerosis, Alzheimer's disease, brain tumors, and more). Using tractography it is possible to evaluate white matter integrity and disruption of connectivity in disease by focusing on important connections between different brain areas. To perform tractography effectively there exists a need to identify areas of crossing tracts, and to find the directions of the different tracts. The method proposed herein optimizes the information available from DTI acquired in clinically feasible time frames. ? ? ?
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